Overview

Dataset statistics

Number of variables60
Number of observations223792
Missing cells5646167
Missing cells (%)42.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.2 MiB
Average record size in memory488.0 B

Variable types

Numeric24
Categorical17
Unsupported19

Alerts

SURVYEAR has constant value "2024"Constant
AGE_12 is highly overall correlated with AGE_6 and 2 other fieldsHigh correlation
AGE_6 is highly overall correlated with AGE_12 and 3 other fieldsHigh correlation
AGYOWNK is highly overall correlated with AGE_12High correlation
AHRSMAIN is highly overall correlated with ATOTHRS and 6 other fieldsHigh correlation
ATOTHRS is highly overall correlated with AHRSMAIN and 6 other fieldsHigh correlation
COWMAIN is highly overall correlated with UNIONHigh correlation
EDUC is highly overall correlated with AGE_6High correlation
ESTSIZE is highly overall correlated with FIRMSIZEHigh correlation
FIRMSIZE is highly overall correlated with ESTSIZEHigh correlation
FTPTMAIN is highly overall correlated with AGE_6 and 5 other fieldsHigh correlation
HRLYEARN is highly overall correlated with AGE_6High correlation
HRSAWAY is highly overall correlated with LFSSTATHigh correlation
LFSSTAT is highly overall correlated with AHRSMAIN and 5 other fieldsHigh correlation
NOC_10 is highly overall correlated with NOC_43High correlation
NOC_43 is highly overall correlated with NOC_10High correlation
PAIDOT is highly overall correlated with XTRAHRSHigh correlation
PAYAWAY is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
SCHOOLN is highly overall correlated with FTPTMAINHigh correlation
TENURE is highly overall correlated with AGE_12High correlation
UHRSMAIN is highly overall correlated with AHRSMAIN and 3 other fieldsHigh correlation
UNION is highly overall correlated with COWMAINHigh correlation
UNPAIDOT is highly overall correlated with XTRAHRSHigh correlation
UTOTHRS is highly overall correlated with AHRSMAIN and 3 other fieldsHigh correlation
XTRAHRS is highly overall correlated with PAIDOT and 1 other fieldsHigh correlation
YABSENT is highly overall correlated with AHRSMAIN and 2 other fieldsHigh correlation
YAWAY is highly overall correlated with LFSSTATHigh correlation
LFSSTAT is highly imbalanced (60.4%)Imbalance
MJH is highly imbalanced (69.6%)Imbalance
PERMTEMP is highly imbalanced (68.8%)Imbalance
SCHOOLN is highly imbalanced (66.7%)Imbalance
AGE_6 has 173568 (77.6%) missing valuesMissing
EVERWORK has 223792 (100.0%) missing valuesMissing
FTPTLAST has 223792 (100.0%) missing valuesMissing
YABSENT has 206264 (92.2%) missing valuesMissing
WKSAWAY has 206264 (92.2%) missing valuesMissing
PAYAWAY has 206264 (92.2%) missing valuesMissing
HRSAWAY has 17528 (7.8%) missing valuesMissing
YAWAY has 196348 (87.7%) missing valuesMissing
PAIDOT has 17528 (7.8%) missing valuesMissing
UNPAIDOT has 17528 (7.8%) missing valuesMissing
XTRAHRS has 17528 (7.8%) missing valuesMissing
WHYPT has 184646 (82.5%) missing valuesMissing
PREVTEN has 223792 (100.0%) missing valuesMissing
DURUNEMP has 223792 (100.0%) missing valuesMissing
FLOWUNEM has 223792 (100.0%) missing valuesMissing
UNEMFTPT has 223792 (100.0%) missing valuesMissing
WHYLEFTO has 223792 (100.0%) missing valuesMissing
WHYLEFTN has 223792 (100.0%) missing valuesMissing
DURJLESS has 223792 (100.0%) missing valuesMissing
AVAILABL has 223792 (100.0%) missing valuesMissing
LKPUBAG has 223792 (100.0%) missing valuesMissing
LKEMPLOY has 223792 (100.0%) missing valuesMissing
LKRELS has 223792 (100.0%) missing valuesMissing
LKATADS has 223792 (100.0%) missing valuesMissing
LKANSADS has 223792 (100.0%) missing valuesMissing
LKOTHERN has 223792 (100.0%) missing valuesMissing
PRIORACT has 223792 (100.0%) missing valuesMissing
YNOLOOK has 223792 (100.0%) missing valuesMissing
TLOLOOK has 223792 (100.0%) missing valuesMissing
SCHOOLN has 10458 (4.7%) missing valuesMissing
AGYOWNK has 140195 (62.6%) missing valuesMissing
EVERWORK is an unsupported type, check if it needs cleaning or further analysisUnsupported
FTPTLAST is an unsupported type, check if it needs cleaning or further analysisUnsupported
PREVTEN is an unsupported type, check if it needs cleaning or further analysisUnsupported
DURUNEMP is an unsupported type, check if it needs cleaning or further analysisUnsupported
FLOWUNEM is an unsupported type, check if it needs cleaning or further analysisUnsupported
UNEMFTPT is an unsupported type, check if it needs cleaning or further analysisUnsupported
WHYLEFTO is an unsupported type, check if it needs cleaning or further analysisUnsupported
WHYLEFTN is an unsupported type, check if it needs cleaning or further analysisUnsupported
DURJLESS is an unsupported type, check if it needs cleaning or further analysisUnsupported
AVAILABL is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKPUBAG is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKEMPLOY is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKRELS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKATADS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKANSADS is an unsupported type, check if it needs cleaning or further analysisUnsupported
LKOTHERN is an unsupported type, check if it needs cleaning or further analysisUnsupported
PRIORACT is an unsupported type, check if it needs cleaning or further analysisUnsupported
YNOLOOK is an unsupported type, check if it needs cleaning or further analysisUnsupported
TLOLOOK is an unsupported type, check if it needs cleaning or further analysisUnsupported
CMA has 141873 (63.4%) zerosZeros
EDUC has 2401 (1.1%) zerosZeros
AHRSMAIN has 17662 (7.9%) zerosZeros
ATOTHRS has 17528 (7.8%) zerosZeros
HRSAWAY has 178820 (79.9%) zerosZeros
PAIDOT has 187593 (83.8%) zerosZeros
UNPAIDOT has 188588 (84.3%) zerosZeros
XTRAHRS has 170981 (76.4%) zerosZeros
WHYPT has 2391 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-21 21:49:51.907436
Analysis finished2024-05-21 21:51:35.117712
Duration1 minute and 43.21 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

REC_NUM
Real number (ℝ)

Distinct104823
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55321.9
Minimum1
Maximum112082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:35.231045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5497
Q127662
median55343
Q382999
95-th percentile105121.45
Maximum112082
Range112081
Interquartile range (IQR)55337

Descriptive statistics

Standard deviation31946.928
Coefficient of variation (CV)0.57747344
Kurtosis-1.1978419
Mean55321.9
Median Absolute Deviation (MAD)27665.5
Skewness0.00093620394
Sum1.2380599 × 1010
Variance1.0206062 × 109
MonotonicityNot monotonic
2024-05-21T17:51:35.369490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41486 4
 
< 0.1%
14293 4
 
< 0.1%
32018 4
 
< 0.1%
66780 4
 
< 0.1%
32010 4
 
< 0.1%
32005 4
 
< 0.1%
32004 4
 
< 0.1%
14292 4
 
< 0.1%
66746 4
 
< 0.1%
32023 4
 
< 0.1%
Other values (104813) 223752
> 99.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
3 2
< 0.1%
4 1
 
< 0.1%
5 4
< 0.1%
7 2
< 0.1%
8 4
< 0.1%
9 3
< 0.1%
10 3
< 0.1%
11 3
< 0.1%
ValueCountFrequency (%)
112082 1
< 0.1%
112081 1
< 0.1%
112080 1
< 0.1%
112078 1
< 0.1%
112075 1
< 0.1%
112074 1
< 0.1%
112073 1
< 0.1%
112072 1
< 0.1%
112068 1
< 0.1%
112067 1
< 0.1%

SURVYEAR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2024
223792 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters895168
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 223792
100.0%

Length

2024-05-21T17:51:35.489267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:35.605036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2024 223792
100.0%

Most occurring characters

ValueCountFrequency (%)
2 447584
50.0%
0 223792
25.0%
4 223792
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 895168
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 447584
50.0%
0 223792
25.0%
4 223792
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 895168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 447584
50.0%
0 223792
25.0%
4 223792
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 895168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 447584
50.0%
0 223792
25.0%
4 223792
25.0%

SURVMNTH
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
4
56903 
3
56213 
2
55649 
1
55027 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters223792
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

Length

2024-05-21T17:51:35.693801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:35.810029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

Most occurring characters

ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 223792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 223792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 56903
25.4%
3 56213
25.1%
2 55649
24.9%
1 55027
24.6%

LFSSTAT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1
206264 
2
 
17528

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters223792
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

Length

2024-05-21T17:51:35.916718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:36.210560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

Most occurring characters

ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 223792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 223792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 206264
92.2%
2 17528
 
7.8%

PROV
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.557862
Minimum10
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:36.291978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q124
median35
Q347
95-th percentile59
Maximum59
Range49
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.472853
Coefficient of variation (CV)0.41880059
Kurtosis-0.83571504
Mean34.557862
Median Absolute Deviation (MAD)11
Skewness0.002373588
Sum7733773
Variance209.46347
MonotonicityNot monotonic
2024-05-21T17:51:36.392717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35 71323
31.9%
24 43581
19.5%
59 26813
 
12.0%
48 17511
 
7.8%
46 15586
 
7.0%
47 13675
 
6.1%
13 10432
 
4.7%
12 10174
 
4.5%
10 10023
 
4.5%
11 4674
 
2.1%
ValueCountFrequency (%)
10 10023
 
4.5%
11 4674
 
2.1%
12 10174
 
4.5%
13 10432
 
4.7%
24 43581
19.5%
35 71323
31.9%
46 15586
 
7.0%
47 13675
 
6.1%
48 17511
 
7.8%
59 26813
 
12.0%
ValueCountFrequency (%)
59 26813
 
12.0%
48 17511
 
7.8%
47 13675
 
6.1%
46 15586
 
7.0%
35 71323
31.9%
24 43581
19.5%
13 10432
 
4.7%
12 10174
 
4.5%
11 4674
 
2.1%
10 10023
 
4.5%

CMA
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.780296
Minimum0
Maximum9
Zeros141873
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:36.493059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8015413
Coefficient of variation (CV)1.5736379
Kurtosis0.69596448
Mean1.780296
Median Absolute Deviation (MAD)0
Skewness1.4071547
Sum398416
Variance7.8486337
MonotonicityNot monotonic
2024-05-21T17:51:36.589649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 141873
63.4%
4 22366
 
10.0%
2 14014
 
6.3%
9 13021
 
5.8%
6 8670
 
3.9%
1 5393
 
2.4%
5 4906
 
2.2%
8 4717
 
2.1%
3 4442
 
2.0%
7 4390
 
2.0%
ValueCountFrequency (%)
0 141873
63.4%
1 5393
 
2.4%
2 14014
 
6.3%
3 4442
 
2.0%
4 22366
 
10.0%
5 4906
 
2.2%
6 8670
 
3.9%
7 4390
 
2.0%
8 4717
 
2.1%
9 13021
 
5.8%
ValueCountFrequency (%)
9 13021
 
5.8%
8 4717
 
2.1%
7 4390
 
2.0%
6 8670
 
3.9%
5 4906
 
2.2%
4 22366
 
10.0%
3 4442
 
2.0%
2 14014
 
6.3%
1 5393
 
2.4%
0 141873
63.4%

AGE_12
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9711026
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:36.689534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8009898
Coefficient of variation (CV)0.46909088
Kurtosis-0.91350801
Mean5.9711026
Median Absolute Deviation (MAD)2
Skewness0.083976733
Sum1336285
Variance7.8455439
MonotonicityNot monotonic
2024-05-21T17:51:36.784121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 26328
11.8%
5 25599
11.4%
4 25501
11.4%
7 23615
10.6%
8 23446
10.5%
3 21919
9.8%
9 21460
9.6%
2 18033
8.1%
10 17161
7.7%
1 10272
 
4.6%
Other values (2) 10458
 
4.7%
ValueCountFrequency (%)
1 10272
 
4.6%
2 18033
8.1%
3 21919
9.8%
4 25501
11.4%
5 25599
11.4%
6 26328
11.8%
7 23615
10.6%
8 23446
10.5%
9 21460
9.6%
10 17161
7.7%
ValueCountFrequency (%)
12 3562
 
1.6%
11 6896
 
3.1%
10 17161
7.7%
9 21460
9.6%
8 23446
10.5%
7 23615
10.6%
6 26328
11.8%
5 25599
11.4%
4 25501
11.4%
3 21919
9.8%

AGE_6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing173568
Missing (%)77.6%
Infinite0
Infinite (%)0.0%
Mean4.1128743
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:36.876444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5672483
Coefficient of variation (CV)0.38105914
Kurtosis-1.0531227
Mean4.1128743
Median Absolute Deviation (MAD)1
Skewness-0.32907566
Sum206565
Variance2.4562674
MonotonicityNot monotonic
2024-05-21T17:51:36.974438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 13666
 
6.1%
4 11317
 
5.1%
5 8253
 
3.7%
2 7616
 
3.4%
3 6716
 
3.0%
1 2656
 
1.2%
(Missing) 173568
77.6%
ValueCountFrequency (%)
1 2656
 
1.2%
2 7616
3.4%
3 6716
3.0%
4 11317
5.1%
5 8253
3.7%
6 13666
6.1%
ValueCountFrequency (%)
6 13666
6.1%
5 8253
3.7%
4 11317
5.1%
3 6716
3.0%
2 7616
3.4%
1 2656
 
1.2%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2
112798 
1
110994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters223792
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

Length

2024-05-21T17:51:37.079301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:37.187779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

Most occurring characters

ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 223792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

Most occurring scripts

ValueCountFrequency (%)
Common 223792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 112798
50.4%
1 110994
49.6%

MARSTAT
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9049251
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:37.269868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.2125426
Coefficient of variation (CV)0.7616522
Kurtosis-1.5631514
Mean2.9049251
Median Absolute Deviation (MAD)1
Skewness0.54188697
Sum650099
Variance4.8953447
MonotonicityNot monotonic
2024-05-21T17:51:37.366291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 105253
47.0%
6 66582
29.8%
2 34639
 
15.5%
5 9273
 
4.1%
4 5576
 
2.5%
3 2469
 
1.1%
ValueCountFrequency (%)
1 105253
47.0%
2 34639
 
15.5%
3 2469
 
1.1%
4 5576
 
2.5%
5 9273
 
4.1%
6 66582
29.8%
ValueCountFrequency (%)
6 66582
29.8%
5 9273
 
4.1%
4 5576
 
2.5%
3 2469
 
1.1%
2 34639
 
15.5%
1 105253
47.0%

EDUC
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8362899
Minimum0
Maximum6
Zeros2401
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:37.457538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4535159
Coefficient of variation (CV)0.37888584
Kurtosis-0.50544572
Mean3.8362899
Median Absolute Deviation (MAD)1
Skewness-0.51062883
Sum858531
Variance2.1127085
MonotonicityNot monotonic
2024-05-21T17:51:37.544947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 81335
36.3%
5 51133
22.8%
2 38556
17.2%
6 25461
 
11.4%
1 13535
 
6.0%
3 11371
 
5.1%
0 2401
 
1.1%
ValueCountFrequency (%)
0 2401
 
1.1%
1 13535
 
6.0%
2 38556
17.2%
3 11371
 
5.1%
4 81335
36.3%
5 51133
22.8%
6 25461
 
11.4%
ValueCountFrequency (%)
6 25461
 
11.4%
5 51133
22.8%
4 81335
36.3%
3 11371
 
5.1%
2 38556
17.2%
1 13535
 
6.0%
0 2401
 
1.1%

MJH
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1.0
211687 
2.0
 
12105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 211687
94.6%
2.0 12105
 
5.4%

Length

2024-05-21T17:51:37.646137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:37.755541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 211687
94.6%
2.0 12105
 
5.4%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 211687
31.5%
2 12105
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
1 211687
47.3%
2 12105
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 211687
31.5%
2 12105
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 211687
31.5%
2 12105
 
1.8%

EVERWORK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

FTPTLAST
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

COWMAIN
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2.0
160833 
1.0
62959 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 160833
71.9%
1.0 62959
 
28.1%

Length

2024-05-21T17:51:37.846109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:37.956434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 160833
71.9%
1.0 62959
 
28.1%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 160833
24.0%
1 62959
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
2 160833
35.9%
1 62959
 
14.1%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 160833
24.0%
1 62959
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 160833
24.0%
1 62959
 
9.4%

IMMIG
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
3
172647 
2
32815 
1
18330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters223792
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

Length

2024-05-21T17:51:38.049489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:38.162996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

Most occurring characters

ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 223792
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 223792
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 223792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 172647
77.1%
2 32815
 
14.7%
1 18330
 
8.2%

NAICS_21
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.386167
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:38.261557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median14
Q317
95-th percentile21
Maximum21
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9172817
Coefficient of variation (CV)0.36734054
Kurtosis-0.95739598
Mean13.386167
Median Absolute Deviation (MAD)4
Skewness-0.25493332
Sum2995717
Variance24.179659
MonotonicityNot monotonic
2024-05-21T17:51:38.370829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
17 34360
15.4%
10 25704
11.5%
16 20868
9.3%
21 16747
 
7.5%
14 15531
 
6.9%
6 14384
 
6.4%
19 12419
 
5.5%
11 11423
 
5.1%
12 10859
 
4.9%
7 10722
 
4.8%
Other values (11) 50775
22.7%
ValueCountFrequency (%)
1 1705
 
0.8%
2 636
 
0.3%
3 192
 
0.1%
4 4473
 
2.0%
5 2198
 
1.0%
6 14384
6.4%
7 10722
4.8%
8 9748
 
4.4%
9 7468
 
3.3%
10 25704
11.5%
ValueCountFrequency (%)
21 16747
7.5%
20 7355
 
3.3%
19 12419
 
5.5%
18 8037
 
3.6%
17 34360
15.4%
16 20868
9.3%
15 6176
 
2.8%
14 15531
6.9%
13 2787
 
1.2%
12 10859
 
4.9%

NOC_10
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1109423
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:38.482080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6155265
Coefficient of variation (CV)0.51175035
Kurtosis-1.2232017
Mean5.1109423
Median Absolute Deviation (MAD)2
Skewness-0.024113358
Sum1143788
Variance6.840979
MonotonicityNot monotonic
2024-05-21T17:51:38.574128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 48882
21.8%
2 37464
16.7%
8 33304
14.9%
5 30025
13.4%
4 18976
 
8.5%
3 18548
 
8.3%
1 18439
 
8.2%
10 9707
 
4.3%
6 4317
 
1.9%
9 4130
 
1.8%
ValueCountFrequency (%)
1 18439
 
8.2%
2 37464
16.7%
3 18548
 
8.3%
4 18976
 
8.5%
5 30025
13.4%
6 4317
 
1.9%
7 48882
21.8%
8 33304
14.9%
9 4130
 
1.8%
10 9707
 
4.3%
ValueCountFrequency (%)
10 9707
 
4.3%
9 4130
 
1.8%
8 33304
14.9%
7 48882
21.8%
6 4317
 
1.9%
5 30025
13.4%
4 18976
 
8.5%
3 18548
 
8.3%
2 37464
16.7%
1 18439
 
8.2%

NOC_43
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.443416
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:38.689469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median23
Q334
95-th percentile40
Maximum43
Range42
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.566916
Coefficient of variation (CV)0.55993776
Kurtosis-1.4361758
Mean22.443416
Median Absolute Deviation (MAD)11
Skewness-0.15083385
Sum5022657
Variance157.92739
MonotonicityNot monotonic
2024-05-21T17:51:38.806207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 20074
 
9.0%
35 16680
 
7.5%
33 15766
 
7.0%
20 11486
 
5.1%
2 10269
 
4.6%
9 10125
 
4.5%
8 9718
 
4.3%
36 8793
 
3.9%
7 8280
 
3.7%
11 7507
 
3.4%
Other values (33) 105094
47.0%
ValueCountFrequency (%)
1 787
 
0.4%
2 10269
4.6%
3 3493
 
1.6%
4 3890
 
1.7%
5 4734
2.1%
6 4607
2.1%
7 8280
3.7%
8 9718
4.3%
9 10125
4.5%
10 1186
 
0.5%
ValueCountFrequency (%)
43 1510
 
0.7%
42 5707
 
2.6%
41 2490
 
1.1%
40 2275
 
1.0%
39 1855
 
0.8%
38 5648
 
2.5%
37 2183
 
1.0%
36 8793
3.9%
35 16680
7.5%
34 20074
9.0%

YABSENT
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing206264
Missing (%)92.2%
Memory size3.4 MiB
1.0
5876 
3.0
5749 
2.0
3845 
0.0
2058 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters52584
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 5876
 
2.6%
3.0 5749
 
2.6%
2.0 3845
 
1.7%
0.0 2058
 
0.9%
(Missing) 206264
92.2%

Length

2024-05-21T17:51:38.917972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:39.036841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5876
33.5%
3.0 5749
32.8%
2.0 3845
21.9%
0.0 2058
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 19586
37.2%
. 17528
33.3%
1 5876
 
11.2%
3 5749
 
10.9%
2 3845
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35056
66.7%
Other Punctuation 17528
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19586
55.9%
1 5876
 
16.8%
3 5749
 
16.4%
2 3845
 
11.0%
Other Punctuation
ValueCountFrequency (%)
. 17528
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19586
37.2%
. 17528
33.3%
1 5876
 
11.2%
3 5749
 
10.9%
2 3845
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19586
37.2%
. 17528
33.3%
1 5876
 
11.2%
3 5749
 
10.9%
2 3845
 
7.3%

WKSAWAY
Real number (ℝ)

MISSING 

Distinct99
Distinct (%)0.6%
Missing206264
Missing (%)92.2%
Infinite0
Infinite (%)0.0%
Mean15.117013
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:39.160382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q320
95-th percentile64
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.949399
Coefficient of variation (CV)1.5181173
Kurtosis4.2507262
Mean15.117013
Median Absolute Deviation (MAD)2
Skewness2.1332623
Sum264971
Variance526.6749
MonotonicityNot monotonic
2024-05-21T17:51:39.290022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5927
 
2.6%
2 1915
 
0.9%
3 1048
 
0.5%
4 726
 
0.3%
99 541
 
0.2%
6 404
 
0.2%
8 389
 
0.2%
5 388
 
0.2%
52 364
 
0.2%
12 313
 
0.1%
Other values (89) 5513
 
2.5%
(Missing) 206264
92.2%
ValueCountFrequency (%)
1 5927
2.6%
2 1915
 
0.9%
3 1048
 
0.5%
4 726
 
0.3%
5 388
 
0.2%
6 404
 
0.2%
7 213
 
0.1%
8 389
 
0.2%
9 157
 
0.1%
10 264
 
0.1%
ValueCountFrequency (%)
99 541
0.2%
98 2
 
< 0.1%
97 1
 
< 0.1%
96 5
 
< 0.1%
95 6
 
< 0.1%
94 4
 
< 0.1%
93 5
 
< 0.1%
92 7
 
< 0.1%
91 2
 
< 0.1%
90 8
 
< 0.1%

PAYAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing206264
Missing (%)92.2%
Memory size3.4 MiB
2.0
9741 
1.0
7787 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters52584
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 9741
 
4.4%
1.0 7787
 
3.5%
(Missing) 206264
92.2%

Length

2024-05-21T17:51:39.404350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:39.518008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 9741
55.6%
1.0 7787
44.4%

Most occurring characters

ValueCountFrequency (%)
. 17528
33.3%
0 17528
33.3%
2 9741
18.5%
1 7787
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35056
66.7%
Other Punctuation 17528
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17528
50.0%
2 9741
27.8%
1 7787
22.2%
Other Punctuation
ValueCountFrequency (%)
. 17528
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17528
33.3%
0 17528
33.3%
2 9741
18.5%
1 7787
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17528
33.3%
0 17528
33.3%
2 9741
18.5%
1 7787
14.8%

UHRSMAIN
Real number (ℝ)

HIGH CORRELATION 

Distinct364
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.96127
Minimum1
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:39.629435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile120
Q1350
median380
Q3400
95-th percentile450
Maximum990
Range989
Interquartile range (IQR)50

Descriptive statistics

Standard deviation106.80535
Coefficient of variation (CV)0.30259793
Kurtosis3.6427966
Mean352.96127
Median Absolute Deviation (MAD)20
Skewness-0.4643672
Sum78989909
Variance11407.383
MonotonicityNot monotonic
2024-05-21T17:51:39.760036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 91168
40.7%
375 29157
 
13.0%
350 20220
 
9.0%
300 7908
 
3.5%
200 6261
 
2.8%
320 4524
 
2.0%
150 3610
 
1.6%
250 3462
 
1.5%
500 3338
 
1.5%
450 2988
 
1.3%
Other values (354) 51156
22.9%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 5
 
< 0.1%
7 2
 
< 0.1%
10 110
< 0.1%
15 7
 
< 0.1%
19 1
 
< 0.1%
20 274
0.1%
25 44
 
< 0.1%
ValueCountFrequency (%)
990 62
< 0.1%
980 8
 
< 0.1%
970 2
 
< 0.1%
960 26
< 0.1%
940 1
 
< 0.1%
920 2
 
< 0.1%
910 5
 
< 0.1%
900 22
 
< 0.1%
880 4
 
< 0.1%
875 1
 
< 0.1%

AHRSMAIN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct555
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.48373
Minimum0
Maximum990
Zeros17662
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:39.888618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1250
median375
Q3400
95-th percentile500
Maximum990
Range990
Interquartile range (IQR)150

Descriptive statistics

Standard deviation151.45088
Coefficient of variation (CV)0.46674412
Kurtosis0.79421158
Mean324.48373
Median Absolute Deviation (MAD)50
Skewness-0.49983083
Sum72616864
Variance22937.368
MonotonicityNot monotonic
2024-05-21T17:51:40.022303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 60358
27.0%
375 17681
 
7.9%
0 17662
 
7.9%
350 12869
 
5.8%
320 7736
 
3.5%
300 7515
 
3.4%
200 4976
 
2.2%
500 4900
 
2.2%
450 4413
 
2.0%
240 4329
 
1.9%
Other values (545) 81353
36.4%
ValueCountFrequency (%)
0 17662
7.9%
1 2
 
< 0.1%
5 11
 
< 0.1%
7 1
 
< 0.1%
10 127
 
0.1%
12 3
 
< 0.1%
15 24
 
< 0.1%
20 278
 
0.1%
22 1
 
< 0.1%
25 46
 
< 0.1%
ValueCountFrequency (%)
990 120
0.1%
980 18
 
< 0.1%
975 3
 
< 0.1%
970 4
 
< 0.1%
960 31
 
< 0.1%
950 3
 
< 0.1%
940 7
 
< 0.1%
935 2
 
< 0.1%
930 1
 
< 0.1%
920 12
 
< 0.1%

FTPTMAIN
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1.0
184646 
2.0
39146 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 184646
82.5%
2.0 39146
 
17.5%

Length

2024-05-21T17:51:40.138369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:40.246330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 184646
82.5%
2.0 39146
 
17.5%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 184646
27.5%
2 39146
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
1 184646
41.3%
2 39146
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 184646
27.5%
2 39146
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 184646
27.5%
2 39146
 
5.8%

UTOTHRS
Real number (ℝ)

HIGH CORRELATION 

Distinct420
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.21911
Minimum1
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:40.357634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile120
Q1350
median400
Q3400
95-th percentile500
Maximum990
Range989
Interquartile range (IQR)50

Descriptive statistics

Standard deviation111.87459
Coefficient of variation (CV)0.31057371
Kurtosis3.7129917
Mean360.21911
Median Absolute Deviation (MAD)25
Skewness-0.1581791
Sum80614154
Variance12515.923
MonotonicityNot monotonic
2024-05-21T17:51:40.487068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 88143
39.4%
375 27885
 
12.5%
350 19326
 
8.6%
300 7413
 
3.3%
200 5783
 
2.6%
320 4359
 
1.9%
500 3937
 
1.8%
450 3423
 
1.5%
150 3341
 
1.5%
250 3210
 
1.4%
Other values (410) 56972
25.5%
ValueCountFrequency (%)
1 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 5
 
< 0.1%
7 2
 
< 0.1%
10 101
 
< 0.1%
15 7
 
< 0.1%
19 1
 
< 0.1%
20 255
0.1%
25 42
 
< 0.1%
ValueCountFrequency (%)
990 113
0.1%
980 9
 
< 0.1%
970 4
 
< 0.1%
960 27
 
< 0.1%
950 10
 
< 0.1%
940 3
 
< 0.1%
930 7
 
< 0.1%
920 3
 
< 0.1%
910 7
 
< 0.1%
900 43
 
< 0.1%

ATOTHRS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct575
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.01677
Minimum0
Maximum990
Zeros17528
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:40.616114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1260
median375
Q3400
95-th percentile535
Maximum990
Range990
Interquartile range (IQR)140

Descriptive statistics

Standard deviation155.58256
Coefficient of variation (CV)0.47001411
Kurtosis0.8851874
Mean331.01677
Median Absolute Deviation (MAD)55
Skewness-0.40083231
Sum74078906
Variance24205.932
MonotonicityNot monotonic
2024-05-21T17:51:40.750005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 58485
26.1%
0 17528
 
7.8%
375 16931
 
7.6%
350 12363
 
5.5%
320 7485
 
3.3%
300 7132
 
3.2%
500 5206
 
2.3%
200 4712
 
2.1%
450 4600
 
2.1%
240 4070
 
1.8%
Other values (565) 85280
38.1%
ValueCountFrequency (%)
0 17528
7.8%
1 2
 
< 0.1%
5 14
 
< 0.1%
7 1
 
< 0.1%
10 115
 
0.1%
12 3
 
< 0.1%
15 23
 
< 0.1%
20 263
 
0.1%
22 1
 
< 0.1%
25 47
 
< 0.1%
ValueCountFrequency (%)
990 182
0.1%
980 18
 
< 0.1%
975 4
 
< 0.1%
970 8
 
< 0.1%
960 38
 
< 0.1%
950 10
 
< 0.1%
940 13
 
< 0.1%
935 1
 
< 0.1%
930 1
 
< 0.1%
920 16
 
< 0.1%

HRSAWAY
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct207
Distinct (%)0.1%
Missing17528
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean14.310398
Minimum0
Maximum990
Zeros178820
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:40.883469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile80
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46.67746
Coefficient of variation (CV)3.2617862
Kurtosis26.655018
Mean14.310398
Median Absolute Deviation (MAD)0
Skewness4.5492512
Sum2951720
Variance2178.7852
MonotonicityNot monotonic
2024-05-21T17:51:41.167656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178820
79.9%
80 6595
 
2.9%
160 2106
 
0.9%
75 2105
 
0.9%
70 1592
 
0.7%
40 1496
 
0.7%
20 1090
 
0.5%
240 939
 
0.4%
150 879
 
0.4%
100 875
 
0.4%
Other values (197) 9767
 
4.4%
(Missing) 17528
 
7.8%
ValueCountFrequency (%)
0 178820
79.9%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
5 92
 
< 0.1%
7 9
 
< 0.1%
8 2
 
< 0.1%
10 589
 
0.3%
11 1
 
< 0.1%
12 8
 
< 0.1%
ValueCountFrequency (%)
990 1
 
< 0.1%
960 1
 
< 0.1%
720 3
 
< 0.1%
700 1
 
< 0.1%
650 1
 
< 0.1%
600 13
< 0.1%
580 1
 
< 0.1%
560 3
 
< 0.1%
520 1
 
< 0.1%
500 7
< 0.1%

YAWAY
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing196348
Missing (%)87.7%
Memory size3.4 MiB
1.0
10283 
3.0
7787 
2.0
5523 
0.0
3270 
4.0
 
581

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82332
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10283
 
4.6%
3.0 7787
 
3.5%
2.0 5523
 
2.5%
0.0 3270
 
1.5%
4.0 581
 
0.3%
(Missing) 196348
87.7%

Length

2024-05-21T17:51:41.284469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:41.398359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10283
37.5%
3.0 7787
28.4%
2.0 5523
20.1%
0.0 3270
 
11.9%
4.0 581
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54888
66.7%
Other Punctuation 27444
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30714
56.0%
1 10283
 
18.7%
3 7787
 
14.2%
2 5523
 
10.1%
4 581
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 27444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30714
37.3%
. 27444
33.3%
1 10283
 
12.5%
3 7787
 
9.5%
2 5523
 
6.7%
4 581
 
0.7%

PAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct208
Distinct (%)0.1%
Missing17528
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean7.7209789
Minimum0
Maximum990
Zeros187593
Zeros (%)83.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:41.518537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34.819115
Coefficient of variation (CV)4.5096762
Kurtosis71.853871
Mean7.7209789
Median Absolute Deviation (MAD)0
Skewness7.1939184
Sum1592560
Variance1212.3707
MonotonicityNot monotonic
2024-05-21T17:51:41.651591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 187593
83.8%
20 1731
 
0.8%
40 1667
 
0.7%
80 1640
 
0.7%
100 1611
 
0.7%
50 1454
 
0.6%
10 1223
 
0.5%
30 1143
 
0.5%
120 903
 
0.4%
60 890
 
0.4%
Other values (198) 6409
 
2.9%
(Missing) 17528
 
7.8%
ValueCountFrequency (%)
0 187593
83.8%
1 7
 
< 0.1%
2 24
 
< 0.1%
3 32
 
< 0.1%
4 6
 
< 0.1%
5 330
 
0.1%
6 3
 
< 0.1%
7 20
 
< 0.1%
8 12
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
990 1
 
< 0.1%
820 1
 
< 0.1%
800 1
 
< 0.1%
750 1
 
< 0.1%
730 1
 
< 0.1%
720 3
< 0.1%
700 5
< 0.1%
695 1
 
< 0.1%
680 1
 
< 0.1%
625 1
 
< 0.1%

UNPAIDOT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct120
Distinct (%)0.1%
Missing17528
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean6.5365842
Minimum0
Maximum990
Zeros188588
Zeros (%)84.3%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:41.784981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.90528
Coefficient of variation (CV)4.4220773
Kurtosis97.834219
Mean6.5365842
Median Absolute Deviation (MAD)0
Skewness7.5065226
Sum1348262
Variance835.51524
MonotonicityNot monotonic
2024-05-21T17:51:41.917062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 188588
84.3%
50 2948
 
1.3%
100 2828
 
1.3%
20 1780
 
0.8%
40 1421
 
0.6%
30 1298
 
0.6%
10 969
 
0.4%
150 962
 
0.4%
60 882
 
0.4%
80 881
 
0.4%
Other values (110) 3707
 
1.7%
(Missing) 17528
 
7.8%
ValueCountFrequency (%)
0 188588
84.3%
2 15
 
< 0.1%
3 9
 
< 0.1%
4 2
 
< 0.1%
5 205
 
0.1%
6 3
 
< 0.1%
7 10
 
< 0.1%
8 8
 
< 0.1%
10 969
 
0.4%
12 6
 
< 0.1%
ValueCountFrequency (%)
990 3
 
< 0.1%
980 1
 
< 0.1%
880 1
 
< 0.1%
800 3
 
< 0.1%
750 2
 
< 0.1%
650 1
 
< 0.1%
630 1
 
< 0.1%
600 8
< 0.1%
560 1
 
< 0.1%
550 2
 
< 0.1%

XTRAHRS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct232
Distinct (%)0.1%
Missing17528
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean14.257563
Minimum0
Maximum990
Zeros170981
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:42.063031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile100
Maximum990
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.704272
Coefficient of variation (CV)3.1354778
Kurtosis42.22781
Mean14.257563
Median Absolute Deviation (MAD)0
Skewness5.2395814
Sum2940822
Variance1998.472
MonotonicityNot monotonic
2024-05-21T17:51:42.208274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 170981
76.4%
100 4367
 
2.0%
50 4209
 
1.9%
20 3259
 
1.5%
40 2954
 
1.3%
80 2487
 
1.1%
30 2250
 
1.0%
10 1955
 
0.9%
60 1754
 
0.8%
200 1453
 
0.6%
Other values (222) 10595
 
4.7%
(Missing) 17528
 
7.8%
ValueCountFrequency (%)
0 170981
76.4%
1 5
 
< 0.1%
2 32
 
< 0.1%
3 40
 
< 0.1%
4 7
 
< 0.1%
5 466
 
0.2%
6 2
 
< 0.1%
7 26
 
< 0.1%
8 19
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
990 4
< 0.1%
980 1
 
< 0.1%
880 1
 
< 0.1%
840 1
 
< 0.1%
820 2
 
< 0.1%
800 4
< 0.1%
750 3
< 0.1%
730 1
 
< 0.1%
720 4
< 0.1%
700 6
< 0.1%

WHYPT
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing184646
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean4.0778113
Minimum0
Maximum7
Zeros2391
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:42.319171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7402759
Coefficient of variation (CV)0.42676715
Kurtosis0.27523815
Mean4.0778113
Median Absolute Deviation (MAD)1
Skewness-0.62272419
Sum159630
Variance3.0285601
MonotonicityNot monotonic
2024-05-21T17:51:42.415929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 14609
 
6.5%
5 9774
 
4.4%
7 3530
 
1.6%
2 2715
 
1.2%
6 2481
 
1.1%
0 2391
 
1.1%
3 1826
 
0.8%
1 1820
 
0.8%
(Missing) 184646
82.5%
ValueCountFrequency (%)
0 2391
 
1.1%
1 1820
 
0.8%
2 2715
 
1.2%
3 1826
 
0.8%
4 14609
6.5%
5 9774
4.4%
6 2481
 
1.1%
7 3530
 
1.6%
ValueCountFrequency (%)
7 3530
 
1.6%
6 2481
 
1.1%
5 9774
4.4%
4 14609
6.5%
3 1826
 
0.8%
2 2715
 
1.2%
1 1820
 
0.8%
0 2391
 
1.1%

TENURE
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.695391
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:42.540415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median58
Q3153
95-th percentile240
Maximum240
Range239
Interquartile range (IQR)134

Descriptive statistics

Standard deviation82.755065
Coefficient of variation (CV)0.92262338
Kurtosis-0.9275381
Mean89.695391
Median Absolute Deviation (MAD)47
Skewness0.73438903
Sum20073111
Variance6848.4008
MonotonicityNot monotonic
2024-05-21T17:51:42.675845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 28186
 
12.6%
6 4010
 
1.8%
7 3847
 
1.7%
8 3725
 
1.7%
5 3716
 
1.7%
4 3355
 
1.5%
2 3310
 
1.5%
9 3248
 
1.5%
3 3238
 
1.4%
10 3047
 
1.4%
Other values (230) 164110
73.3%
ValueCountFrequency (%)
1 2621
1.2%
2 3310
1.5%
3 3238
1.4%
4 3355
1.5%
5 3716
1.7%
6 4010
1.8%
7 3847
1.7%
8 3725
1.7%
9 3248
1.5%
10 3047
1.4%
ValueCountFrequency (%)
240 28186
12.6%
239 261
 
0.1%
238 251
 
0.1%
237 246
 
0.1%
236 289
 
0.1%
235 271
 
0.1%
234 260
 
0.1%
233 216
 
0.1%
232 204
 
0.1%
231 185
 
0.1%

PREVTEN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

HRLYEARN
Real number (ℝ)

HIGH CORRELATION 

Distinct5838
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.336227
Minimum5.77
Maximum216.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:42.809591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.77
5-th percentile15.5
Q121
median29.33
Q343
95-th percentile67.859
Maximum216.35
Range210.58
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.498759
Coefficient of variation (CV)0.53875341
Kurtosis7.5171409
Mean34.336227
Median Absolute Deviation (MAD)9.73
Skewness2.064211
Sum7684172.9
Variance342.2041
MonotonicityNot monotonic
2024-05-21T17:51:42.935998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 7211
 
3.2%
25 6889
 
3.1%
18 4712
 
2.1%
30 4701
 
2.1%
22 4298
 
1.9%
17 4231
 
1.9%
15 3958
 
1.8%
21 3576
 
1.6%
23 3534
 
1.6%
16 3480
 
1.6%
Other values (5828) 177202
79.2%
ValueCountFrequency (%)
5.77 1
 
< 0.1%
5.82 1
 
< 0.1%
6.67 1
 
< 0.1%
6.73 1
 
< 0.1%
6.92 4
< 0.1%
7.03 1
 
< 0.1%
7.14 1
 
< 0.1%
7.21 5
< 0.1%
7.22 1
 
< 0.1%
7.35 1
 
< 0.1%
ValueCountFrequency (%)
216.35 1
 
< 0.1%
214.1 1
 
< 0.1%
208.33 1
 
< 0.1%
206.73 1
 
< 0.1%
205.68 1
 
< 0.1%
205.29 1
 
< 0.1%
205.13 3
 
< 0.1%
200 1
 
< 0.1%
197.8 2
 
< 0.1%
192.31 29
< 0.1%

UNION
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
3.0
149896 
1.0
69402 
2.0
 
4494

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 149896
67.0%
1.0 69402
31.0%
2.0 4494
 
2.0%

Length

2024-05-21T17:51:43.049385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:43.161401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 149896
67.0%
1.0 69402
31.0%
2.0 4494
 
2.0%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
3 149896
33.5%
1 69402
 
15.5%
2 4494
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
3 149896
22.3%
1 69402
 
10.3%
2 4494
 
0.7%

PERMTEMP
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1.0
200416 
3.0
 
13074
4.0
 
7555
2.0
 
2747

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 200416
89.6%
3.0 13074
 
5.8%
4.0 7555
 
3.4%
2.0 2747
 
1.2%

Length

2024-05-21T17:51:43.259408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:43.372639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 200416
89.6%
3.0 13074
 
5.8%
4.0 7555
 
3.4%
2.0 2747
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
1 200416
44.8%
3 13074
 
2.9%
4 7555
 
1.7%
2 2747
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
1 200416
29.9%
3 13074
 
1.9%
4 7555
 
1.1%
2 2747
 
0.4%

ESTSIZE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2.0
74001 
1.0
67963 
3.0
46275 
4.0
35553 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 74001
33.1%
1.0 67963
30.4%
3.0 46275
20.7%
4.0 35553
15.9%

Length

2024-05-21T17:51:43.471862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:43.587631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 74001
33.1%
1.0 67963
30.4%
3.0 46275
20.7%
4.0 35553
15.9%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
2 74001
 
16.5%
1 67963
 
15.2%
3 46275
 
10.3%
4 35553
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
2 74001
 
11.0%
1 67963
 
10.1%
3 46275
 
6.9%
4 35553
 
5.3%

FIRMSIZE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
4.0
113823 
1.0
37302 
2.0
36718 
3.0
35949 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters671376
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
4.0 113823
50.9%
1.0 37302
 
16.7%
2.0 36718
 
16.4%
3.0 35949
 
16.1%

Length

2024-05-21T17:51:43.694451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:43.810701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 113823
50.9%
1.0 37302
 
16.7%
2.0 36718
 
16.4%
3.0 35949
 
16.1%

Most occurring characters

ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447584
66.7%
Other Punctuation 223792
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223792
50.0%
4 113823
25.4%
1 37302
 
8.3%
2 36718
 
8.2%
3 35949
 
8.0%
Other Punctuation
ValueCountFrequency (%)
. 223792
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 671376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 223792
33.3%
0 223792
33.3%
4 113823
17.0%
1 37302
 
5.6%
2 36718
 
5.5%
3 35949
 
5.4%

DURUNEMP
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

FLOWUNEM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

UNEMFTPT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

WHYLEFTO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

WHYLEFTN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

DURJLESS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

AVAILABL
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKPUBAG
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKEMPLOY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKRELS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKATADS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKANSADS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

LKOTHERN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

PRIORACT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

YNOLOOK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

TLOLOOK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing223792
Missing (%)100.0%
Memory size3.4 MiB

SCHOOLN
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing10458
Missing (%)4.7%
Memory size3.4 MiB
1.0
193020 
2.0
 
15623
3.0
 
4691

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters640002
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 193020
86.2%
2.0 15623
 
7.0%
3.0 4691
 
2.1%
(Missing) 10458
 
4.7%

Length

2024-05-21T17:51:43.918797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:44.030175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 193020
90.5%
2.0 15623
 
7.3%
3.0 4691
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 213334
33.3%
0 213334
33.3%
1 193020
30.2%
2 15623
 
2.4%
3 4691
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 426668
66.7%
Other Punctuation 213334
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 213334
50.0%
1 193020
45.2%
2 15623
 
3.7%
3 4691
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 213334
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 640002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 213334
33.3%
0 213334
33.3%
1 193020
30.2%
2 15623
 
2.4%
3 4691
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 640002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 213334
33.3%
0 213334
33.3%
1 193020
30.2%
2 15623
 
2.4%
3 4691
 
0.7%

EFAMTYPE
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0587599
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:44.122089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.0997483
Coefficient of variation (CV)1.0081025
Kurtosis1.2123404
Mean5.0587599
Median Absolute Deviation (MAD)1
Skewness1.6053804
Sum1132110
Variance26.007432
MonotonicityNot monotonic
2024-05-21T17:51:44.224508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 61757
27.6%
2 45776
20.5%
1 36267
16.2%
18 17923
 
8.0%
4 16127
 
7.2%
14 9304
 
4.2%
5 8361
 
3.7%
8 7498
 
3.4%
6 6975
 
3.1%
15 4004
 
1.8%
Other values (8) 9800
 
4.4%
ValueCountFrequency (%)
1 36267
16.2%
2 45776
20.5%
3 61757
27.6%
4 16127
 
7.2%
5 8361
 
3.7%
6 6975
 
3.1%
7 2079
 
0.9%
8 7498
 
3.4%
9 2784
 
1.2%
10 1483
 
0.7%
ValueCountFrequency (%)
18 17923
8.0%
17 431
 
0.2%
16 436
 
0.2%
15 4004
 
1.8%
14 9304
4.2%
13 282
 
0.1%
12 180
 
0.1%
11 2125
 
0.9%
10 1483
 
0.7%
9 2784
 
1.2%

AGYOWNK
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing140195
Missing (%)62.6%
Memory size3.4 MiB
1.0
27722 
2.0
26048 
3.0
15845 
4.0
13982 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters250791
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 27722
 
12.4%
2.0 26048
 
11.6%
3.0 15845
 
7.1%
4.0 13982
 
6.2%
(Missing) 140195
62.6%

Length

2024-05-21T17:51:44.345310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T17:51:44.463529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 27722
33.2%
2.0 26048
31.2%
3.0 15845
19.0%
4.0 13982
16.7%

Most occurring characters

ValueCountFrequency (%)
. 83597
33.3%
0 83597
33.3%
1 27722
 
11.1%
2 26048
 
10.4%
3 15845
 
6.3%
4 13982
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 167194
66.7%
Other Punctuation 83597
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83597
50.0%
1 27722
 
16.6%
2 26048
 
15.6%
3 15845
 
9.5%
4 13982
 
8.4%
Other Punctuation
ValueCountFrequency (%)
. 83597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 250791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 83597
33.3%
0 83597
33.3%
1 27722
 
11.1%
2 26048
 
10.4%
3 15845
 
6.3%
4 13982
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 83597
33.3%
0 83597
33.3%
1 27722
 
11.1%
2 26048
 
10.4%
3 15845
 
6.3%
4 13982
 
5.6%

FINALWT
Real number (ℝ)

Distinct2061
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.9469
Minimum1
Maximum3403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2024-05-21T17:51:44.586613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62
Q1136
median220
Q3370
95-th percentile958
Maximum3403
Range3402
Interquartile range (IQR)234

Descriptive statistics

Standard deviation288.18378
Coefficient of variation (CV)0.91793798
Kurtosis6.2998819
Mean313.9469
Median Absolute Deviation (MAD)100
Skewness2.2736159
Sum70258805
Variance83049.894
MonotonicityNot monotonic
2024-05-21T17:51:44.713028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 845
 
0.4%
131 835
 
0.4%
126 834
 
0.4%
140 815
 
0.4%
134 811
 
0.4%
124 809
 
0.4%
133 804
 
0.4%
138 800
 
0.4%
129 799
 
0.4%
130 795
 
0.4%
Other values (2051) 215645
96.4%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 3
 
< 0.1%
7 8
< 0.1%
8 6
< 0.1%
9 4
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
3403 1
< 0.1%
2795 1
< 0.1%
2737 1
< 0.1%
2736 1
< 0.1%
2683 1
< 0.1%
2667 1
< 0.1%
2666 1
< 0.1%
2654 1
< 0.1%
2653 2
< 0.1%
2652 2
< 0.1%

Interactions

2024-05-21T17:51:27.920659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:19.045025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.857477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.628994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:27.320328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.688948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:33.364236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:36.177949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:39.348773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:42.388790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:45.191611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:48.471698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.912924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:55.146828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:58.225387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:01.420645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.899233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.863276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.688503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.661914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:16.395609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:19.199932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:22.013828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.816908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:28.041291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:19.187256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.973481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:50:27.434314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:50:33.481834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:36.291724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:50:42.501077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:45.301356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:48.586792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:52.048980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:55.262313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:58.338636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:01.539670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:05.053824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.985053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.810319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.776632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:16.503076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:19.318024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:22.127650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.941261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:28.163058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:19.328531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:50:20.932873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:23.683758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.423095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:29.700127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:32.443667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.220585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.254787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.384107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:51:09.742440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-21T17:51:23.895459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:26.768516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.033504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.048614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:23.798427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.535460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:29.820736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:32.556840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.338292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.388841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.505456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.264435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:47.531382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:50.783242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.027323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.256718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:00.597088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:03.769335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.021861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:09.855551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:12.819527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:15.598657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.256222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.169871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.009645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:26.890696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.159088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.164587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:23.916807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.650218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:29.950763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:32.665184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.455499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.521832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.626091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.376562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:47.719606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:50.881275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.145584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.454677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:00.718506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:03.941750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.135174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:09.971834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:12.931652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:15.710642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.364705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.299056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.124234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.019809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.275759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.271275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.020085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.752585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.073553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:32.778695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.563363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.647745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.739498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.478008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:47.848373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.001419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.274854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.578183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:00.825460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.086251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.238733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.076751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.036953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:15.813965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.471166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.417398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.231259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.145286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.404022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.389943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.139875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.868486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.211147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:32.893523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.681159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.784184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.865123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.597254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:47.990525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.364601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.680001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.725341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:00.947088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.258348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.376096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.200567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.161777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:15.936076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.585439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.548659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.350558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.450849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.524486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.501890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.251582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:26.980442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.345391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:33.014604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.797591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:38.916089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:41.988245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.707195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:48.119092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.495319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.794054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.854312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:01.062864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.425396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.495741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.319251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.277252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:16.048949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.691781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.658270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.462937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.563812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.643700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.610935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.380029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:27.087454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.455667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:33.131897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:35.906980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:39.039824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:42.105404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:44.813296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:48.241750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.634357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:54.903184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:57.981125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:01.173682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.611677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.611655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.435381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.407874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:16.158474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.797128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.769646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.570992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.676790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:30.771487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:21.735362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:24.507620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:27.202756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:30.575118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:33.244803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:36.046138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:39.181872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:42.253528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:45.073694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:48.361652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:51.779941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:55.025664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:50:58.105224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:01.299449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:04.755218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:07.738878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:10.558492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:13.538590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:16.278398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:18.909545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:21.890685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:24.695089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-21T17:51:27.795232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-21T17:51:44.886037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGE_12AGE_6AGYOWNKAHRSMAINATOTHRSCMACOWMAINEDUCEFAMTYPEESTSIZEFINALWTFIRMSIZEFTPTMAINHRLYEARNHRSAWAYIMMIGLFSSTATMARSTATMJHNAICS_21NOC_10NOC_43PAIDOTPAYAWAYPERMTEMPPROVREC_NUMSCHOOLNSEXSURVMNTHTENUREUHRSMAINUNIONUNPAIDOTUTOTHRSWHYPTWKSAWAYXTRAHRSYABSENTYAWAY
AGE_121.0000.9490.5000.1060.100-0.0010.1530.070-0.0570.095-0.1180.0670.4220.2360.0110.1580.041-0.4420.033-0.030-0.092-0.0950.0040.2690.135-0.0210.0020.4040.0270.0000.5050.1160.1030.0740.1070.2060.0230.0560.2950.090
AGE_60.9491.0000.0420.4040.4020.0510.1780.586-0.2360.1330.0540.0580.5540.5510.0800.0640.046-0.4430.048-0.056-0.228-0.2700.1070.2990.1380.020-0.0020.4060.0210.0000.3090.4940.1340.1340.4900.1310.3480.1650.2310.098
AGYOWNK0.5000.0421.0000.0730.0760.0490.035-0.0840.3770.0120.0550.0110.0120.004-0.0470.1180.1300.0020.0160.003-0.024-0.017-0.0290.2320.023-0.007-0.0030.0420.0480.0000.261-0.0030.0190.0370.0030.161-0.2970.0090.3580.086
AHRSMAIN0.1060.4040.0731.0000.9670.0060.1950.088-0.0540.0770.0040.0640.7230.259-0.3160.0470.793-0.1210.077-0.200-0.004-0.0260.3091.0000.1470.033-0.0010.3190.2400.0180.1150.6700.0930.2750.6330.095NaN0.4321.0000.074
ATOTHRS0.1000.4020.0760.9671.0000.0060.1870.095-0.0540.0750.0030.0620.7020.245-0.3060.0410.803-0.1170.225-0.184-0.006-0.0270.2991.0000.1450.036-0.0000.3190.2320.0180.1030.6390.0900.2670.6760.105NaN0.4191.0000.071
CMA-0.0010.0510.0490.0060.0061.0000.0950.1280.0440.0690.4970.0350.0230.068-0.0200.2420.019-0.0100.0270.019-0.095-0.100-0.0470.0670.0210.3240.0030.0300.0010.003-0.013-0.0110.0800.025-0.011-0.006-0.008-0.0160.0630.073
COWMAIN0.1530.1780.0350.1950.1870.0951.000-0.2570.0200.3260.0850.3540.057-0.271-0.0600.0830.0590.0870.031-0.4450.2110.212-0.0190.1970.1230.048-0.0020.0700.1760.000-0.1880.1420.595-0.1120.1270.024-0.074-0.0920.0870.104
EDUC0.0700.586-0.0840.0880.0950.128-0.2571.000-0.0780.1220.0520.1010.2590.4320.0220.1350.017-0.2190.0490.160-0.362-0.395-0.0350.1960.088-0.0060.0020.2630.1060.0000.0670.0440.1010.1920.0570.0530.0650.1120.1510.075
EFAMTYPE-0.057-0.2360.377-0.054-0.0540.0440.020-0.0781.0000.0310.0150.0270.106-0.089-0.0110.0800.031-0.0250.0220.0030.0340.041-0.0260.0830.0400.012-0.0010.0920.3060.000-0.034-0.0610.040-0.031-0.062-0.034-0.000-0.0410.2000.066
ESTSIZE0.0950.1330.0120.0770.0750.0690.3260.1220.0311.0000.0420.5090.1560.3160.0290.0460.042-0.0820.0270.027-0.119-0.1280.0630.1970.047-0.0340.0010.0750.0320.0030.1600.0760.2150.0740.069-0.0240.0570.1010.0460.073
FINALWT-0.1180.0540.0550.0040.0030.4970.0850.0520.0150.0421.0000.0070.0140.037-0.0220.0670.0120.0650.004-0.025-0.024-0.027-0.0240.0210.0100.2720.0030.0240.0220.010-0.077-0.0100.0390.012-0.013-0.026-0.036-0.0080.0230.008
FIRMSIZE0.0670.0580.0110.0640.0620.0350.3540.1010.0270.5090.0071.0000.1130.2390.0310.0220.043-0.0580.0220.062-0.104-0.1120.0530.2030.044-0.0220.0000.0310.0400.0020.1570.0050.2400.0930.002-0.0200.0550.1060.0510.079
FTPTMAIN0.4220.5540.0120.7230.7020.0230.0570.2590.1060.1560.0140.1131.000-0.366-0.0660.0420.0110.1860.0780.1160.1060.134-0.0940.2450.3070.008-0.0010.5410.1330.000-0.210-0.6830.090-0.092-0.651NaN-0.148-0.1370.2190.148
HRLYEARN0.2360.5510.0040.2590.2450.068-0.2710.432-0.0890.3160.0370.239-0.3661.0000.0350.0560.019-0.2810.037-0.010-0.338-0.3830.0620.3130.0910.063-0.0010.1930.1270.0000.3450.2580.1610.2410.2410.035-0.0210.2210.1110.106
HRSAWAY0.0110.080-0.047-0.316-0.306-0.020-0.0600.022-0.0110.029-0.0220.031-0.0660.0351.0000.0071.000-0.0200.0310.023-0.011-0.012-0.0110.0000.0090.014-0.0010.0240.0200.0090.0370.0200.0330.0000.024-0.030NaN-0.0110.0000.091
IMMIG0.1580.0640.1180.0470.0410.2420.0830.1350.0800.0460.0670.0220.0420.0560.0071.0000.0120.1570.0160.0050.0230.0220.0190.0660.032-0.1500.0010.0450.0140.0000.061-0.0400.0490.038-0.046-0.001-0.0120.0400.1100.036
LFSSTAT0.0410.0460.1300.7930.8030.0190.0590.0170.0310.0420.0120.0430.0110.0191.0000.0121.000-0.0270.0320.0270.0050.005NaN1.0000.012-0.0050.0030.0130.0660.0330.043-0.0160.067NaN-0.024-0.031NaNNaN1.0001.000
MARSTAT-0.442-0.4430.002-0.121-0.117-0.0100.087-0.219-0.025-0.0820.065-0.0580.186-0.281-0.0200.157-0.0271.0000.0220.0270.1320.144-0.0180.1290.077-0.0540.0010.2470.0920.000-0.271-0.1520.069-0.073-0.146-0.079-0.093-0.0680.1630.068
MJH0.0330.0480.0160.0770.2250.0270.0310.0490.0220.0270.0040.0220.0780.0370.0310.0160.0320.0221.0000.059-0.016-0.013-0.0150.0050.0370.0250.0020.0210.0360.001-0.052-0.0770.0090.0020.1910.043-0.041-0.0110.0400.019
NAICS_21-0.030-0.0560.003-0.200-0.1840.019-0.4450.1600.0030.027-0.0250.0620.116-0.0100.0230.0050.0270.0270.0591.000-0.296-0.286-0.0690.2100.122-0.031-0.0020.2030.3860.0000.005-0.2930.3250.042-0.265-0.0310.056-0.0260.1360.102
NOC_10-0.092-0.228-0.024-0.004-0.006-0.0950.211-0.3620.034-0.119-0.024-0.1040.106-0.338-0.0110.0230.0050.132-0.016-0.2961.0000.9900.1160.2410.1400.0140.0010.2120.4920.003-0.1040.0690.279-0.1750.0630.032-0.040-0.0380.1780.128
NOC_43-0.095-0.270-0.017-0.026-0.027-0.1000.212-0.3950.041-0.128-0.027-0.1120.134-0.383-0.0120.0220.0050.144-0.013-0.2860.9901.0000.1150.2360.1280.0150.0010.2130.4910.000-0.1180.0450.284-0.1920.0400.027-0.040-0.0510.1620.127
PAIDOT0.0040.107-0.0290.3090.299-0.047-0.019-0.035-0.0260.063-0.0240.053-0.0940.062-0.0110.019NaN-0.018-0.015-0.0690.1160.1151.0000.0000.0210.025-0.0000.0310.0830.0050.0320.1370.053-0.0350.128-0.010NaN0.6960.0000.013
PAYAWAY0.2690.2990.2321.0001.0000.0670.1970.1960.0830.1970.0210.2030.2450.3130.0000.0661.0000.1290.0050.2100.2410.2360.0001.0000.180-0.009-0.0090.2140.0790.070-0.255-0.1800.158NaN-0.176-0.0010.311NaN0.3900.000
PERMTEMP0.1350.1380.0230.1470.1450.0210.1230.0880.0400.0470.0100.0440.3070.0910.0090.0320.0120.0770.0370.1220.1400.1280.0210.1801.000-0.036-0.0020.1920.0510.008-0.242-0.2320.044-0.034-0.2180.009-0.095-0.0470.1010.082
PROV-0.0210.020-0.0070.0330.0360.3240.048-0.0060.012-0.0340.272-0.0220.0080.0630.014-0.150-0.005-0.0540.025-0.0310.0140.0150.025-0.009-0.0361.0000.0000.0360.0150.001-0.0250.0530.0800.0150.057-0.015-0.0100.0290.0910.084
REC_NUM0.002-0.002-0.003-0.001-0.0000.003-0.0020.002-0.0010.0010.0030.000-0.001-0.001-0.0010.0010.0030.0010.002-0.0020.0010.001-0.000-0.009-0.0020.0001.0000.0010.0030.0170.001-0.0020.0050.005-0.001-0.008-0.0070.0040.0060.007
SCHOOLN0.4040.4060.0420.3190.3190.0300.0700.2630.0920.0750.0240.0310.5410.1930.0240.0450.0130.2470.0210.2030.2120.2130.0310.2140.1920.0360.0011.0000.0560.005-0.253-0.3710.069-0.059-0.363-0.129-0.132-0.0900.2760.103
SEX0.0270.0210.0480.2400.2320.0010.1760.1060.3060.0320.0220.0400.1330.1270.0200.0140.0660.0920.0360.3860.4920.4910.0830.0790.0510.0150.0030.0561.0000.0000.011-0.2740.0660.031-0.257-0.0560.176-0.0490.2730.074
SURVMNTH0.0000.0000.0000.0180.0180.0030.0000.0000.0000.0030.0100.0020.0000.0000.0090.0000.0330.0000.0010.0000.0030.0000.0050.0700.0080.0010.0170.0050.0001.000-0.0060.0050.0000.0010.0050.012-0.0460.0020.0750.090
TENURE0.5050.3090.2610.1150.103-0.013-0.1880.067-0.0340.160-0.0770.157-0.2100.3450.0370.0610.043-0.271-0.0520.005-0.104-0.1180.032-0.255-0.242-0.0250.001-0.2530.011-0.0061.0000.1390.1440.1080.1230.0470.0990.1010.1800.077
UHRSMAIN0.1160.494-0.0030.6700.639-0.0110.1420.044-0.0610.076-0.0100.005-0.6830.2580.020-0.040-0.016-0.152-0.077-0.2930.0690.0450.137-0.180-0.2320.053-0.002-0.371-0.2740.0050.1391.0000.1380.0430.9420.1000.0630.1370.2080.095
UNION0.1030.1340.0190.0930.0900.0800.5950.1010.0400.2150.0390.2400.0900.1610.0330.0490.0670.0690.0090.3250.2790.2840.0530.1580.0440.0800.0050.0690.0660.0000.1440.1381.000-0.0200.0300.018-0.086-0.0970.0620.058
UNPAIDOT0.0740.1340.0370.2750.2670.025-0.1120.192-0.0310.0740.0120.093-0.0920.2410.0000.038NaN-0.0730.0020.042-0.175-0.192-0.035NaN-0.0340.0150.005-0.0590.0310.0010.1080.043-0.0201.0000.042-0.026NaN0.6730.0000.009
UTOTHRS0.1070.4900.0030.6330.676-0.0110.1270.057-0.0620.069-0.0130.002-0.6510.2410.024-0.046-0.024-0.1460.191-0.2650.0630.0400.128-0.176-0.2180.057-0.001-0.363-0.2570.0050.1230.9420.0300.0421.0000.1090.0560.1280.2050.091
WHYPT0.2060.1310.1610.0950.105-0.0060.0240.053-0.034-0.024-0.026-0.020NaN0.035-0.030-0.001-0.031-0.0790.043-0.0310.0320.027-0.010-0.0010.009-0.015-0.008-0.129-0.0560.0120.0470.1000.018-0.0260.1091.000-0.054-0.0230.3390.183
WKSAWAY0.0230.348-0.297NaNNaN-0.008-0.0740.065-0.0000.057-0.0360.055-0.148-0.021NaN-0.012NaN-0.093-0.0410.056-0.040-0.040NaN0.311-0.095-0.010-0.007-0.1320.176-0.0460.0990.063-0.086NaN0.056-0.0541.000NaN0.3300.000
XTRAHRS0.0560.1650.0090.4320.419-0.016-0.0920.112-0.0410.101-0.0080.106-0.1370.221-0.0110.040NaN-0.068-0.011-0.026-0.038-0.0510.696NaN-0.0470.0290.004-0.090-0.0490.0020.1010.137-0.0970.6730.128-0.023NaN1.0000.0000.016
YABSENT0.2950.2310.3581.0001.0000.0630.0870.1510.2000.0460.0230.0510.2190.1110.0000.1101.0000.1630.0400.1360.1780.1620.0000.3900.1010.0910.0060.2760.2730.0750.1800.2080.0620.0000.2050.3390.3300.0001.0000.000
YAWAY0.0900.0980.0860.0740.0710.0730.1040.0750.0660.0730.0080.0790.1480.1060.0910.0361.0000.0680.0190.1020.1280.1270.0130.0000.0820.0840.0070.1030.0740.0900.0770.0950.0580.0090.0910.1830.0000.0160.0001.000

Missing values

2024-05-21T17:51:31.115338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-21T17:51:32.404297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-21T17:51:34.763196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

REC_NUMSURVYEARSURVMNTHLFSSTATPROVCMAAGE_12AGE_6SEXMARSTATEDUCMJHEVERWORKFTPTLASTCOWMAINIMMIGNAICS_21NOC_10NOC_43YABSENTWKSAWAYPAYAWAYUHRSMAINAHRSMAINFTPTMAINUTOTHRSATOTHRSHRSAWAYYAWAYPAIDOTUNPAIDOTXTRAHRSWHYPTTENUREPREVTENHRLYEARNUNIONPERMTEMPESTSIZEFIRMSIZEDURUNEMPFLOWUNEMUNEMFTPTWHYLEFTOWHYLEFTNDURJLESSAVAILABLLKPUBAGLKEMPLOYLKRELSLKATADSLKANSADSLKOTHERNPRIORACTYNOLOOKTLOLOOKSCHOOLNEFAMTYPEAGYOWNKFINALWT
2320241110012.01621.0NaNNaN2.0310.07.033.0NaNNaNNaN250.0220.02.0250.0220.00.0NaN0.00.00.06.041.0NaN15.003.01.01.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.04NaN34
3420241135412NaN1241.0NaNNaN2.038.010.042.0NaNNaNNaN400.0400.01.0400.0400.00.0NaN0.00.00.0NaN178.0NaN24.503.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaN275
452024113506NaN1141.0NaNNaN2.036.08.035.0NaNNaNNaN450.0420.01.0450.0420.00.0NaN0.00.00.0NaN212.0NaN28.003.01.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0231
6720241124210NaN1561.0NaNNaN2.0314.03.013.0NaNNaNNaN375.0375.01.0375.0375.00.0NaN0.00.00.0NaN240.0NaN24.743.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN159
7820241159036.01241.0NaNNaN2.036.08.035.0NaNNaNNaN400.0840.01.0400.0840.00.0NaN440.00.0440.0NaN17.0NaN40.003.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN131
8920241159011NaN1121.0NaNNaN2.0319.07.034.0NaNNaNNaN400.0840.01.0400.0840.00.0NaN440.00.0440.0NaN152.0NaN18.503.01.04.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaN139
101120241159010NaN1141.0NaNNaN1.0316.03.013.0NaNNaNNaN350.0200.01.0350.0200.0150.00.00.00.00.0NaN240.0NaN36.331.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN299
11122024122404NaN2141.0NaNNaN2.037.01.04.00.03.02.0400.00.01.0400.00.0NaNNaNNaNNaNNaNNaN1.0NaN34.753.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN390
131420241159012NaN1121.0NaNNaN2.0311.08.036.0NaNNaNNaN400.0400.01.0400.0400.00.0NaN70.00.070.0NaN10.0NaN34.951.01.01.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5NaN145
16172024113506NaN2151.0NaNNaN1.0217.04.018.0NaNNaNNaN375.0495.01.0375.0495.00.0NaN120.00.0120.0NaN30.0NaN27.001.01.03.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0121
REC_NUMSURVYEARSURVMNTHLFSSTATPROVCMAAGE_12AGE_6SEXMARSTATEDUCMJHEVERWORKFTPTLASTCOWMAINIMMIGNAICS_21NOC_10NOC_43YABSENTWKSAWAYPAYAWAYUHRSMAINAHRSMAINFTPTMAINUTOTHRSATOTHRSHRSAWAYYAWAYPAIDOTUNPAIDOTXTRAHRSWHYPTTENUREPREVTENHRLYEARNUNIONPERMTEMPESTSIZEFIRMSIZEDURUNEMPFLOWUNEMUNEMFTPTWHYLEFTOWHYLEFTNDURJLESSAVAILABLLKPUBAGLKEMPLOYLKRELSLKATADSLKANSADSLKOTHERNPRIORACTYNOLOOKTLOLOOKSCHOOLNEFAMTYPEAGYOWNKFINALWT
44255811206720244113012NaN1451.0NaNNaN2.0314.03.011.0NaNNaNNaN375.0600.01.0375.0600.00.0NaN0.0100.0100.0NaN43.0NaN46.152.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1NaN203
4425591120682024415994NaN1661.0NaNNaN2.0314.05.019.0NaNNaNNaN550.0550.01.0550.0550.00.0NaN0.00.00.0NaN44.0NaN48.953.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN1528
4425631120722024414668NaN1121.0NaNNaN2.037.02.09.0NaNNaNNaN400.0480.01.0400.0480.00.0NaN80.00.080.0NaN172.0NaN32.641.01.03.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN179
4425641120732024413505NaN1621.0NaNNaN2.031.09.040.0NaNNaNNaN450.0500.01.0450.0500.00.0NaN50.00.050.0NaN57.0NaN25.003.01.01.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN180
4425651120742024412426NaN1241.0NaNNaN2.039.08.038.0NaNNaNNaN400.0400.01.0400.0400.00.0NaN0.00.00.0NaN59.0NaN16.001.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0199
4425661120752024414888NaN1141.0NaNNaN2.0211.08.036.0NaNNaNNaN400.0400.01.0400.0400.00.0NaN0.00.00.0NaN9.0NaN27.003.01.02.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.063.0531
4425691120782024413544NaN2151.0NaNNaN2.0314.03.011.0NaNNaNNaN400.0400.01.0400.0400.00.0NaN0.00.00.0NaN20.0NaN38.463.01.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.02NaN329
4425711120802024414704NaN1152.0NaNNaN2.0319.07.033.0NaNNaNNaN250.0220.02.0510.0480.0260.00.00.00.00.06.04.0NaN14.003.04.02.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.02NaN160
4425721120812024414887NaN2151.0NaNNaN1.0221.05.022.0NaNNaNNaN375.0375.01.0375.0375.00.0NaN0.00.00.0NaN75.0NaN43.081.01.04.04.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.032.0753
4425731120822024414709NaN2541.0NaNNaN2.0316.07.031.0NaNNaNNaN400.0600.01.0400.0600.00.0NaN0.00.00.0NaN142.0NaN21.953.01.02.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.01NaN165